Reservoir Permeability: Innovative Measurement Based on Novel Concept
Bibliographic record
Abstract
Abstract A new model of permeability to clarify the true behavior of fluid flow around producing wells is presented. A two-dimensional representation of fluid flow in the reservoir is inadequate in many ways. The direction of fluid flow in the reservoir is neither horizontal nor vertical, and consequently the traditional use of these two permeability components in determining fluid flow characteristics has been misleading. Rather, fluids flow from all parts of the reservoir and converge to the wellbore, a very small spot in a big reservoir. The flow pattern towards the wellbore takes a conical shape where the base of the cone at the reservoir boundary and the head is at the wellbore. In many cases, the flow reduces to the perforations which are even smaller "ports" compared to the huge reservoir. The need for a three-dimensional permeability becomes significant, and this term, complete with a technique for measuring it, is introduced here. A new method developed for determining the 3-D (conical) permeability presented and discussed. The proposed method is based on Darcy’s Law employed in a hemispherical scheme as the released gas flows from the probe through the sample in this pattern. The derivation of the model used along with the numerical technique utilized for solving are presented as well. Predictions of the model for air flow into a porous system are presented that appear to conform to the authors’ vision of fluid flow in petroleum reservoirs. This new 3-D (tapering) permeability term should enhance the accuracy of the models used to represent fluid flow in porous media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".